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  <h1>Source code for torch_tensorrt._TRTModuleNext</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">from</span> <span class="nn">operator</span> <span class="kn">import</span> <span class="n">truediv</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
<span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>


<div class="viewcode-block" id="TRTModuleNext"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext">[docs]</a><span class="k">class</span> <span class="nc">TRTModuleNext</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;TRTModuleNext is a PyTorch module which encompasses an arbitrary TensorRT Engine.</span>

<span class="sd">    This module is backed by the Torch-TensorRT runtime and is fully compatibile with both</span>
<span class="sd">    FX / Python deployments (just ``import torch_tensorrt`` as part of the application) as</span>
<span class="sd">    well as TorchScript / C++ deployments since TRTModule can be passed to ``torch.jit.trace``</span>
<span class="sd">    and then saved.</span>

<span class="sd">    The forward function is simpily forward(*args: torch.Tensor) -&gt; Tuple[torch.Tensor] where</span>
<span class="sd">    the internal implementation is ``return Tuple(torch.ops.tensorrt.execute_engine(list(inputs), self.engine))``</span>

<span class="sd">    &gt; Note: TRTModuleNext only supports engines built with explict batch</span>

<span class="sd">    Attributes:</span>
<span class="sd">        name (str): Name of module (for easier debugging)</span>
<span class="sd">        engine (torch.classess.tensorrt.Engine): Torch-TensorRT TensorRT Engine instance, manages [de]serialization, device configuration, profiling</span>
<span class="sd">        input_binding_names (List[str]): List of input TensorRT engine binding names in the order they would be passed to the TRT modules</span>
<span class="sd">        output_binding_names (List[str]): List of output TensorRT engine binding names in the order they should be returned</span>
<span class="sd">    &quot;&quot;&quot;</span>

<div class="viewcode-block" id="TRTModuleNext.__init__"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">serialized_engine</span><span class="p">:</span> <span class="nb">bytearray</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(),</span>
        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
        <span class="n">input_binding_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[],</span>
        <span class="n">output_binding_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[],</span>
        <span class="n">target_device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
    <span class="p">):</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;__init__ method for torch_tensorrt.TRTModuleNext</span>

<span class="sd">        Takes a name, target device, serialized TensorRT engine, and binding names / order and constructs</span>
<span class="sd">        a PyTorch ``torch.nn.Module`` around it.</span>

<span class="sd">        If binding names are not provided, it is assumed that the engine binding names follow the following convention:</span>

<span class="sd">            - [symbol].[index in input / output array]</span>
<span class="sd">                - ex. [x.0, x.1, x.2] -&gt; [y.0]</span>

<span class="sd">        Args:</span>
<span class="sd">            name (str): Name for module</span>
<span class="sd">            serialized_engine (bytearray): Serialized TensorRT engine in the form of a bytearray</span>
<span class="sd">            input_binding_names (List[str]): List of input TensorRT engine binding names in the order they would be passed to the TRT modules</span>
<span class="sd">            output_binding_names (List[str]): List of output TensorRT engine binding names in the order they should be returned</span>
<span class="sd">            target_device: (torch_tensorrt.Device): Device to instantiate TensorRT engine on. Must be a compatible device i.e. same GPU model / compute capability as was used to build the engine</span>

<span class="sd">        Example:</span>

<span class="sd">            ..code-block:: py</span>

<span class="sd">                with io.BytesIO() as engine_bytes:</span>
<span class="sd">                    engine_bytes.write(trt_engine.serialize())</span>
<span class="sd">                    engine_str = engine_bytes.getvalue()</span>

<span class="sd">                trt_module = TRTModule(</span>
<span class="sd">                    engine_str,</span>
<span class="sd">                    engine_name=&quot;my_module&quot;,</span>
<span class="sd">                    input_names=[&quot;x&quot;],</span>
<span class="sd">                    output_names=[&quot;output&quot;],</span>
<span class="sd">                )</span>

<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="s2">&quot;TRTModuleNext should be considered experimental stability, APIs are subject to change. Note: TRTModuleNext only supports engines built with explict batch&quot;</span>
        <span class="p">)</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TRTModuleNext</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">serialized_engine</span><span class="p">,</span> <span class="nb">bytearray</span><span class="p">):</span>
            <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Expected serialized engine as bytearray&quot;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_names</span> <span class="o">=</span> <span class="n">input_binding_names</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_names</span> <span class="o">=</span> <span class="n">output_binding_names</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>

        <span class="k">if</span> <span class="n">serialized_engine</span> <span class="o">!=</span> <span class="nb">bytearray</span><span class="p">():</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">Engine</span><span class="p">(</span>
                <span class="p">[</span>
                    <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ABI_VERSION</span><span class="p">(),</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_engine&quot;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span> <span class="k">else</span> <span class="s2">&quot;tensorrt_engine&quot;</span><span class="p">,</span>
                    <span class="n">target_device</span><span class="o">.</span><span class="n">_to_serialized_rt_device</span><span class="p">(),</span>
                    <span class="n">serialized_engine</span><span class="p">,</span>
                    <span class="n">TRTModuleNext</span><span class="o">.</span><span class="n">_pack_binding_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_binding_names</span><span class="p">),</span>
                    <span class="n">TRTModuleNext</span><span class="o">.</span><span class="n">_pack_binding_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_binding_names</span><span class="p">),</span>
                <span class="p">]</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="kc">None</span></div>

<div class="viewcode-block" id="TRTModuleNext.get_extra_state"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.get_extra_state">[docs]</a>    <span class="k">def</span> <span class="nf">get_extra_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_names</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_names</span><span class="p">,</span>
        <span class="p">)</span></div>

<div class="viewcode-block" id="TRTModuleNext.set_extra_state"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.set_extra_state">[docs]</a>    <span class="k">def</span> <span class="nf">set_extra_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">serialized_engine_info</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
            <span class="kn">import</span> <span class="nn">base64</span>

            <span class="n">serialized_engine</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">b64decode</span><span class="p">(</span><span class="n">serialized_engine_info</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">Engine</span><span class="p">(</span>
                <span class="p">[</span>
                    <span class="n">serialized_engine_info</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
                    <span class="n">serialized_engine_info</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
                    <span class="n">serialized_engine_info</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
                    <span class="n">serialized_engine</span><span class="p">,</span>
                    <span class="n">serialized_engine_info</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
                    <span class="n">serialized_engine_info</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span>
                <span class="p">]</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_names</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_names</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span></div>

<div class="viewcode-block" id="TRTModuleNext.forward"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">):</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Implementation of the forward pass for a TensorRT engine</span>

<span class="sd">        Args:</span>
<span class="sd">            *inputs (torch.Tensor): Inputs to the forward function, must all be ``torch.Tensor``</span>

<span class="sd">        Returns:</span>
<span class="sd">            torch.Tensor or Tuple(torch.Tensor): Result of the engine computation</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Engine has not been initalized yet.&quot;</span><span class="p">)</span>

        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_names</span>
        <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Wrong number of inputs, expected </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_binding_names</span><span class="p">)</span><span class="si">}</span><span class="s2"> got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>

        <span class="n">types</span> <span class="o">=</span> <span class="p">[</span><span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">]</span>

        <span class="k">try</span><span class="p">:</span>
            <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">types</span><span class="p">)</span>
        <span class="k">except</span><span class="p">:</span>

            <span class="k">def</span> <span class="nf">is_non_tensor</span><span class="p">(</span><span class="n">i</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="nb">bool</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
                <span class="k">return</span> <span class="ow">not</span> <span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

            <span class="n">non_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">filter</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">types</span><span class="p">),</span> <span class="n">is_non_tensor</span><span class="p">)]</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;TRTModuleNext expects a flattened list of tensors as input, found non tensors: </span><span class="si">{</span><span class="n">non_tensors</span><span class="si">}</span><span class="s2">&quot;</span>
            <span class="p">)</span>

        <span class="n">outputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">execute_engine</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="p">)</span>

        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span></div>

<div class="viewcode-block" id="TRTModuleNext.enable_profiling"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.enable_profiling">[docs]</a>    <span class="k">def</span> <span class="nf">enable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">profiling_results_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Enable the profiler to collect latency information about the execution of the engine</span>

<span class="sd">        Traces can be visualized using https://ui.perfetto.dev/ or compatible alternatives</span>

<span class="sd">        Keyword Arguments:</span>
<span class="sd">            profiling_results_dir (str): Absolute path to the directory to sort results of profiling.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Engine has not been initalized yet.&quot;</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">profiling_results_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">profile_path_prefix</span> <span class="o">=</span> <span class="n">profiling_results_dir</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">enable_profiling</span><span class="p">()</span></div>

<div class="viewcode-block" id="TRTModuleNext.disable_profiling"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.disable_profiling">[docs]</a>    <span class="k">def</span> <span class="nf">disable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Disable the profiler&quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Engine has not been initalized yet.&quot;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">disable_profiling</span><span class="p">()</span></div>

<div class="viewcode-block" id="TRTModuleNext.get_layer_info"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.get_layer_info">[docs]</a>    <span class="k">def</span> <span class="nf">get_layer_info</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get a JSON string containing the layer information encoded by the TensorRT engine in this module</span>

<span class="sd">        Returns:</span>

<span class="sd">            str: A JSON string which contains the layer information of the engine incapsulated in this module</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Engine has not been initalized yet.&quot;</span><span class="p">)</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_engine_layer_info</span><span class="p">()</span></div>

<div class="viewcode-block" id="TRTModuleNext.dump_layer_info"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.TRTModuleNext.dump_layer_info">[docs]</a>    <span class="k">def</span> <span class="nf">dump_layer_info</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Dump layer information encoded by the TensorRT engine in this module to STDOUT&quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Engine has not been initalized yet.&quot;</span><span class="p">)</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">dump_engine_layer_info</span><span class="p">()</span></div>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">_pack_binding_names</span><span class="p">(</span><span class="n">binding_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
        <span class="n">delim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">SERIALIZED_ENGINE_BINDING_DELIM</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
        <span class="k">return</span> <span class="n">delim</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">binding_names</span><span class="p">)</span></div>
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